Book Image

Feature Store for Machine Learning

By : Jayanth Kumar M J
Book Image

Feature Store for Machine Learning

By: Jayanth Kumar M J

Overview of this book

Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started. Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You’ll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time. By the end of this book, you’ll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1 – Why Do We Need a Feature Store?
Section 1 – Why Do We Need a Feature Store?
Free Chapter
2
Chapter 1: An Overview of the Machine Learning Life Cycle
Chapter 1: An Overview of the Machine Learning Life Cycle
Technical requirements
The ML life cycle in practice
An ideal world versus the real world
The most time-consuming stages of ML
Summary
3
Chapter 2: What Problems Do Feature Stores Solve?
Chapter 2: What Problems Do Feature Stores Solve?
Importance of features in production
Ways to bring features to production
Common problems with the approaches used for bringing features to production
Feature stores to the rescue
Philosophy behind feature stores
Summary
Further reading
4
Section 2 – A Feature Store in Action
Section 2 – A Feature Store in Action
5
Chapter 3: Feature Store Fundamentals, Terminology, and Usage
Chapter 3: Feature Store Fundamentals, Terminology, and Usage
Technical requirements
Introduction to Feast and installation
Feast terminology and definitions
Feast initialization
Feast usage
Feast behind the scenes
Summary
Further reading
6
Chapter 4: Adding Feature Store to ML Models
Chapter 4: Adding Feature Store to ML Models
Technical requirements
Creating Feast resources in AWS
Feast initialization for AWS
Exploring the ML life cycle with Feast
Summary
References
7
Chapter 5: Model Training and Inference
Chapter 5: Model Training and Inference
Prerequisites
Technical requirements
Model training with the feature store
Model packaging
Batch model inference with Feast
Online model inference with Feast
Handling changes to the feature set during development
Summary
Further reading
8
Chapter 6: Model to Production and Beyond
Chapter 6: Model to Production and Beyond
Technical requirements
Setting up Airflow for orchestration
Productionizing the batch model pipeline
Productionizing an online model pipeline
Beyond model production
Summary
9
Section 3 – Alternatives, Best Practices, and a Use Case
Section 3 – Alternatives, Best Practices, and a Use Case
10
Chapter 7: Feast Alternatives and ML Best Practices
Chapter 7: Feast Alternatives and ML Best Practices
Technical requirements
The available feature stores on the market
Feature management with SageMaker Feature Store
ML best practices
Summary
11
Chapter 8: Use Case – Customer Churn Prediction
Chapter 8: Use Case – Customer Churn Prediction
Technical requirements
Infrastructure setup
Introduction to the problem and the dataset
Data processing and feature engineering
Feature group definitions and feature ingestion
Model training
Model prediction
Feature monitoring
Model monitoring
Summary
Why subscribe?
12
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Productionizing the batch model pipeline

In Chapter 4, Adding Feature Store to ML Models, for model training, we used the features ingested by the feature engineering notebook. We also created a model-scoring notebook that fetches features for a set of customers from Feast and runs predictions for it using the trained model. For the sake of the experiment, let's assume that the raw data freshness latency is a day. That means the features need to be regenerated once a day, and the model needs to score customers against those features once a day and store the results in an S3 bucket for consumption. To achieve this, thanks to our early organization and decoupling of stages, all we need to do is run the feature engineering and model scoring notebook/Python script once a day consecutively. Now that we also have a tool to perform this, let's go ahead and schedule this workflow in the Airflow environment.

The following figure displays how we will be operationalizing the batch...

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